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Aging is accompanied by possible cognitive impairment, as well as a reduction in nitric oxide (NO) bioavailability, insulin resistance, and vitamin D deficiency. Among these Nitric oxide exhibits a dual role in brain biology, where both low and high levels can have detrimental effects on memory and neurotoxicity. In this study we aimed to investigate the role of nitric oxide, insulin resistance, and vitamin D in cognitive function of elderly individuals. This case-control study involved participants aged ≥ 60 years who were under observation in the Birjand Longitudinal Aging Study. A total of 40 participants were selected as controls, while 64 participants with cognitive impairment were identified as cases. The cases were further subdivided according to their Mini-Mental State Examination (MMSE) scores into two groups: mild and moderate cognitive impairment. Cognitive function in both cases and controls was assessed using the MMSE. The levels of total NOx, vitamin D, insulin, and blood glucose were measured using ELISA and spectrophotometric immunoassays. Insulin resistance indices, such as homeostasis model assessment of insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) were calculated. Serum NOx levels were significantly lower in participants with cognitive impairment compared to the control group (p < 0.01). However, the mean (SD) levels of serum vitamin D, insulin, and glucose, as well as the insulin resistance markers, showed no significant differences between the groups. A positive correlation was observed between serum NOx levels and MMSE scores. NO may be an essential factor for normal brain function, as serum NOx levels were significantly higher in controls compared to those with cognitive impairment, and there was a positive correlation with MMSE scores. Other metabolic and nutritional markers, including vitamin D and insulin resistance, did not demonstrate a significant effect. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the commonest worldwide neurodegenerative disorder. Nevertheless, it usually face difficulties to guarantee a secured initial diagnosis. For this reason, neurologists are in dire need for developing potential biomarkers that could be relied upon confidentially in early diagnosis of AD. Hopefully, this will open the gate for novel modifying therapy to fight with all their might. In this current study, we aimed to correlate plasma levels of tau and Aβ with the changes that occur in hippocampal volume and thickness of retinal fiber layers in patients who clinically diagnosed with AD spectrum. A cross-sectional study enrolled 60 AD patients who fulfilled inclusion and exclusion criteria were subjected to cognitive, radiologic, laboratory and optical coherence tomography (OCT) assessments. Results: Tau, Aβ1–40, and Aβ1–40/Aβ1–42 ratio are significant discriminators of AD at cutoff values of >23.45, > 84.4, and > 1.95, respectively. MRI hippocampal volume in both right and left sides are also good discriminators of AD at cutoff values of ≤ 2.997, and ≤ 2.994, respectively. A significant correlations were reported between tau with Aβ1–40, Aβ1–42, MMSE and MRI right and left hippocampal volumes. On comparing moderate versus mild AD, there was a high significant levels of tau, Aβ1–42, Aβ1–40/Aβ1–42 ratio. Conclusions: We clarify that several biomarkers could be potentially used for confirming the diagnosis of AD. Assessment of plasma amyloid level, detection of hippocampal atrophy and retinal nerve fiber layer thickness changes are promising tools for early diagnosis of AD. [ABSTRACT FROM AUTHOR]

Recent studies indicate a potential link between oral health and cognitive function; however, long-term associations remain unclear. This study aimed to identify oral health factors that predict changes in cognitive function among older adults over time. The study included 583 independent older adults (201 male, 382 female) with a mean age of 72.7 years. Cognitive function was assessed using Mini-Mental State Examination (MMSE) at baseline and follow-up over two years, with relative change in MMSE (rMMSE) calculated. Baseline oral health variables included number of remaining teeth, masticatory performance, occlusal force, oral diadochokinesis and tongue pressure. Physical performance and confounding factors were also considered. The relationship between rMMSE and the variables was analyzed using Pearson’s correlation, Mann-Whitney U test, and multiple linear regression analysis. Cognitive function declined in 196 subjects, with rMMSE significantly correlated with oral diadochokinesis. Regression analysis revealed significant associations between cognitive changes and oral diadochokinesis (p = 0.020) and knee extension strength as a physical performance (p = 0.047). Our findings suggest that cognitive decline may be indicated by declines in both physical and oral motor performance. Incorporating oral diadochokinesis testing into health screenings could aid early detection of cognitive decline, improving outcomes and reducing healthcare burdens. [ABSTRACT FROM AUTHOR]

Age-related hearing loss is common among older individuals and is linked to cognitive impairment and a decrease in overall quality of life. Although hearing aids enhance auditory capabilities, their influence on cognitive performance in the Indian context has not been thoroughly investigated. This study investigates the cognitive benefits of hearing aids in elderly Indian patients with age-related hearing loss. A prospective cohort study was conducted at a tertiary care centre between January 2021 and December 2022. The study included 200 elderly patients who were fitted with behind-the-ear (BTE) or receiver-in-canal (RIC) hearing aids. The assessment of cognitive function was conducted using two standardised tests: the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The assessment of quality of life was conducted using the WHOQOL-OLD questionnaire, while hearing acuity was examined by pure-tone audiometry and speech perception tests. Data were collected at baseline, 6 months, and 18 months. Multiple linear regression analysis identified predictors of cognitive outcomes. There were significant enhancements in MMSE, MoCA, HHIE, and WHOQOL-OLD scores across the 18-month duration (p < 0.001). The pure-tone audiometry thresholds and speech perception scores demonstrated significant improvement (p < 0.001). Regression analysis indicated that baseline cognitive function, hearing ability, and quality of life were significant predictors of cognitive outcomes at 18 months. No severe cognitive impairment or other confounding severe medical conditions were reported. This study revealed that the use of hearing aids has a substantial positive impact on cognitive function, quality of life, and hearing ability in the older Indian population suffering from age-related hearing loss. Timely intervention and the availability of hearing aids are essential for improving cognitive health and overall well-being in this demographic. Future study should look into the consequences and influence of various hearing aid models. [ABSTRACT FROM AUTHOR]

A small set of pilots in underwater acoustics OFDM (UWA-OFDM) systems tends to be insufficient for recovering the channel impulse response (CIR). This may result from the fast changes in the propagation environment and the requirement for high transmission data rates. A previous work, namely PE, considered the received subcarriers whose distances to their closest constellation points below the predetermined threshold T as potential pilots. However, extracting these pilots at the same time gives no chance to use some of them to facilitate searching for others. Fixing threshold T is also another limitation for searching pilot candidates. This paper proposes a reliable pilot search (RPS) method that consists of a multi-iterations pilot searching process with an adaptive threshold T a to improve the estimator in UWA-OFDM. The pilot search process gradually extracts reliable pilots at each iteration based on the threshold T a and evaluates them by performing channel estimation. Our method is compared to the MMSE and PE estimators on a wide range of settings, including the number of channel taps, pilot spacing, and various modulation schemes (i.e., MPSK and MQAM). The experimental results show that the RPS method often outperforms the MMSE and PE methods in terms of bit error rate (BER). [ABSTRACT FROM AUTHOR]

Deficits in the neuroendocrine-immune network in the periphery associated with the onset and progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD) have not been extensively studied. The present study correlatively examines the association between cell-mediated immune responses, stress hormones, amyloid precursor protein (APP) expression, peripheral blood mononuclear cells (PBMC), and intracellular signaling molecules in the pathophysiology of MCI and AD compared to adults. Serum APP, lymphocyte proliferation, total cholinesterase (TChE), butyrylcholinesterase (BChE) activities, cytokines (IL-2, IFN-γ, IL-6, and TNF-α), and intracellular signaling molecules (p-ERK, p-CREB, and p-Akt) were measured in the PBMCs of adult, old, MCI, and AD men and women initially and after 3 years in the same population. An age- and disease-associated decline in mini-mental state examination (MMSE) scores and lymphocyte proliferation of MCI and AD men and women were observed. An age- and disease-related increase in serum APP, cortisol levels, and TChE activity were observed in men and women. Enhanced production of Th1 cytokine, IL-2, pro-inflammatory cytokines, and suppressed intracellular transcription factors may promote the inflammatory environment in MCI and AD patients. The expression of CREB and Akt was lower in MCI and AD men, while the expression of p-ERK was higher, and p-CREB was lower in MCI and AD women after 3 years. These results suggest that changes in specific intracellular signaling pathways may influence alterations in cell-mediated immunity to promote disease progression in MCI and AD patients. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a chronic progressive neurodegenerative disabling disease and involves about 1–3% of the worldwide population over the age of 60. A significant prevalence of psychopathological symptoms has been recorded as most patients with PD developed over their disease course neuropsychiatric symptoms such as depression, anxiety, sleep disorders, psychosis, and cognitive and behavioral abnormalities. These non-motor symptoms, which could appear decades before motor ones, become disturbing symptoms during the later phases of the disease. Hence, the current research aims to study depressive symptoms in Parkinson's disease patients. Thirty-six patients with Parkinson's disease aged from 40 to 65 years (20 males and 16 females) and 36 age and sex-matched controls (19 males and 17 females) were included in the study. Unified Parkinson's Disease Rating Scale (UPDRS), Hoehn and Yahr scale, Schwab and England's scale, Mini-Mental State Examination, Cognitive Ability Screening Instrument, and Hamilton Depression Rating Scale were applied to assess depression in both groups. Results: Patients were 20 males and 16 females (mean age 52.44 ± 7.45), mean duration of Parkinsonism was 3.88 years. The mean value for Hoehn and Yahr scale was 1.97 ± 1.42, for UPDRS T was 42.41 ± 20.91 and Schwab England's scale was 74.77 ± 17.78. Concerning cognition, MMSE was significantly lower among patients 25.33 ± 3.63, than in the control group and CAS total was significantly lower in patients (16 ± 71.35) than in the control group 9.81 ± 84.62. Conclusion: Depressive symptoms are widespread in Parkinson's disease. Depression should be strictly determined and addressed, particularly in patients with more advanced cognitive impairment who are at a higher risk of developing or worsening depression. [ABSTRACT FROM AUTHOR]

Neurodegenerative pathologies such as Alzheimer disease neuropathologic change (ADNC), Lewy body disease (LBD), limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and cerebrovascular disease (CVD) frequently coexist, but little is known about the exact contribution of each pathology to cognitive decline and dementia in subjects with mixed pathologies. We explored the relative cognitive impact of concurrent common and rare neurodegenerative pathologies employing multivariate logistic regression analysis adjusted for age, gender, and level of education. We analyzed a cohort of 6,262 subjects from the National Alzheimer’s Coordinating Center database, ranging from 0 to 6 comorbid neuropathologic findings per individual, where 95.7% of individuals had at least 1 neurodegenerative finding at autopsy and 75.5% had at least 2 neurodegenerative findings. We identified which neuropathologic entities correlate most frequently with one another and demonstrated that the total number of pathologies per individual was directly correlated with cognitive performance as assessed by Clinical Dementia Rating (CDR®) and Mini-Mental State Examination (MMSE). We show that ADNC, LBD, LATE-NC, CVD, hippocampal sclerosis, Pick disease, and FTLD-TDP significantly impact overall cognition as independent variables. More specifically, ADNC significantly affected all assessed cognitive domains, LBD affected attention, processing speed, and language, LATE-NC primarily affected tests related to logical memory and language, while CVD and other less common pathologies (including Pick disease, progressive supranuclear palsy, and corticobasal degeneration) had more variable neurocognitive effects. Additionally, ADNC, LBD, and higher numbers of comorbid neuropathologies were associated with the presence of at least one APOE ε4 allele, and ADNC and higher numbers of neuropathologies were inversely correlated with APOE ε2 alleles. Understanding the mechanisms by which individual and concomitant neuropathologies affect cognition and the degree to which each contributes is an imperative step in the development of biomarkers and disease-modifying therapeutics, particularly as these medical interventions become more targeted and personalized. [ABSTRACT FROM AUTHOR]

Current drugs for Alzheimer’s Disease (AD), such as cholinesterase inhibitors (ChEIs), exert only symptomatic activity. Different psychometric tools are needed to assess cognitive and non-cognitive dimensions during pharmacological treatment. In this pilot study, we monitored 33 mild-AD patients treated with ChEIs. Specifically, we evaluated the effects of 6 months (Group 1 = 17 patients) and 9 months (Group 2 = 16 patients) of ChEIs administration on cognition with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Frontal Assessment Battery (FAB), while depressive symptoms were measured with the Hamilton Depression Rating Scale (HDRS). After 6 months (Group 1), a significant decrease in MoCA performance was detected. After 9 months (Group 2), a significant decrease in MMSE, MoCA, and FAB performance was observed. ChEIs did not modify depressive symptoms. Overall, our data suggest MoCA is a potentially useful tool for evaluating the effectiveness of ChEIs. [ABSTRACT FROM AUTHOR]

Multiple input multiple output (MIMO) system with Millimeter Wave spectrum is currently used in most wireless applications and all cellular system to provides high data rates. with using large antenna array which is possible by decrease the wavelength to achieve high beamforming gain and improve the spectral efficiency. in this paper, used low complexity with hybrid precoding at the transmitting side and combining at the receiver side with limited feedback system, by using the concept of orthogonal matching pursuit (OMP) in single and multi-user cases, and compared the results with analog only beasmstring. The results of simulation showed that when used Minimum Mean Square Error (MMSE) precoders performed better than other hybrid precoding approaches, in addition the MMSE hybrid precoding /combining technique offers higher spectral efficiency compared with analog only beamstring. [ABSTRACT FROM AUTHOR]

Towards the 5Gnetworks and beyond, there are a lot of emerging technologies. These technologies include but not limited to; multiple input multiple output (MIMO), cell-Free networks, and millimeter wave bands. The cellular MIMO can provide a satisfied performance for users except the shadowed and the cell-edge ones. In order to overcome this disadvantage, the cell-Free networks are deployed. Through applications of distributed access points (APs), the cell-Free networks can provide a ubiquitous coverage for users as whole. Therefore, they can provide a high throughput for users. Moreover, the applications of millimeter wave bands can provide a high bandwidth and hence, a high throughput for users. In other words, the application of the cell-Free technology combined with the millimeter wave bands can extremely enlarge the users' throughput. This is the motivation of our manuscript. The purpose of this manuscript is to provide mathematical model and simulation for the cell-Free mMIMO network, operating in the millimeter wave bands. The performance metrics can include; the spectral efficiency (SE), bit error rate (BER), and energy efficiency (EE). It is observed that the centralized cooperation among the APs can let users have a satisfied throughput even the system employs the maximal ratio combining (MRC). Furthermore, all cooperation levels, among APs, can perform well even for non-light of sight (NLOS) environment. [ABSTRACT FROM AUTHOR]

The paper uses the K-graphs learning method to construct weighted, connected, undirected multiple graphs, aiming to reveal intrinsic relationships of speech samples in the inter-frame and intra-frame. To benefit from the learned multiple graphs' property and enhance interpretability, we study the spectral property of speech samples in the joint vertex-frequency domain by using the new graph weight matrix. Moreover, we propose the representation of minimum mean-square error (MMSE) graph spectral magnitude estimator for speech signals residing on undirected multiple graphs. We use the MMSE graph spectral magnitude estimator to improve speech enhancement performance. The numerical simulation results show that the proposed method outperforms the existing methods in graph signal processing (GSP) and the baseline methods for speech enhancement in discrete signal processing (DSP) in terms of PESQ, LLR, output SNR, and STOI results. These results also demonstrate the validity of the learned multiple graphs. [ABSTRACT FROM AUTHOR]

In this paper, low-complexity channel estimators and precoders are proposed for massive multiple-input multiple-output generalized frequency division multiplexing (MIMO-GFDM) systems. In order to combat the effect of non-orthogonality in GFDM, interference-free pilots are used in frequency-domain minimum mean square error (MMSE) channel estimation. Polynomial expansion is used to approximately compute the matrix inverses in the conventional MMSE estimation and precoding, consequently reducing the cubic computational complexity to square order. The degree of the matrix polynomial can be properly selected to get a required trade-off between complexity and estimation/precoding performance. Different weights can be assigned to the terms in the polynomial expansion and be optimized to achieve a minimal mean square error (MSE). Derived limits on the MSE of the proposed estimators can predict their performance in the high E s / N 0 region. Then, we derive a Cramér-Rao lower bound (CRLB) and use it as a benchmark for the estimators. In addition, the related computational complexity and the impacts of the polynomial degree are also investigated. Numerical results show the accuracy of the proposed channel estimators and precoders. [ABSTRACT FROM AUTHOR]

Aim: Human herpesvirus 6 (HHV-6) is neurophilic, and its relationship with Alzheimer's disease (AD) remains controversial. This study aimed to examine the relationships between HHV-6 and cognitive abilities in elderly people aged 60 years or above from communities in Shenzhen. Methods: We recruited participants from 10 community health service centers in Shenzhen. Participants were divided into case and control groups according to Mini-Mental State Examination (MMSE) scale standards and were included in this study with 1:1 matching based on sex and age (± 3 years). The HHV-6 gene was detected by real-time fluorescent quantitative PCR, and the HHV-6 copy number was quantified. Results: A total of 580 participants (cases, n = 290; controls, n = 290), matched for gender and age was included in this study. A positive HHV-6 test was not associated with a significant difference in global cognitive performance (ORadjusted = 1.651, 95% CI = 0.671–4.062). After adjusting for gender, age, education, Pittsburgh Sleep Quality Index (PSQI) score, homocysteine (Hcy) and glycosylated hemoglobin (HbA1c), the results of multiple linear regression showed that there was a statistically negative correlation between HHV-6 copy number and orientation (βadjusted = −0.974, p = 0.013), attention and calculation (βadjusted = −1.840, p < 0.001), and language (βadjusted = −2.267, p < 0.001). The restricted cubic spline (RCS) model results showed that there was a nonlinear dose–response relationship between HHV-6 log10-transformed copies and orientation (poverall = 0.003, pnonliner = 0.045), attention and calculation (poverall < 0.001, pnonliner < 0.001), and language (poverall < 0.001, pnonliner = 0.016). Conclusions: HHV-6 infection significantly associated with orientation, attention and calculation, and language in elderly individuals. [ABSTRACT FROM AUTHOR]

This paper aims to improve the channel estimation (CE) in the indoor visible light communication (VLC) system. We propose a system that depends on a comparison between Deep Neural Networks (DNN) and Kalman Filter (KF) algorithm for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The channel estimation can be evaluated by changing the rate of errors in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved channel estimation and system performance. Thus, the main aim of our proposal is decreasing the error rate by using different estimators. Using the simulation results with the metric parameter of bit error rate (BER) aims to determine the improvement ratio between different systems. The proposed model is trained with OFDM signal samples with labels, where the labels represent the received signal after applying OFDM travelling across the medium. At a BER = 10–3 with DCO-OFDM, the DNN outperforms KF with 1.6 dB (7.6%) at the bit energy per noise (E b / N o) axis. Also, for ACO-OFDM at BER = 10–3, the DNN achieves better results than KF by about 1.3 dB (8.12%) at the (E b / N o). At different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1 dB (~ 11.5%). [ABSTRACT FROM AUTHOR]

This article proposes an improved Newton algorithm as a low complexity signal detection scheme for linear receiver in large scale multiple- input multiple- output (LS-MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system, where a large number of antennas are set up at the base station and active users are with a single antenna system. Data detection for uplink SC-FDMA system is one of the specific challenges due to the significant rise in the dimension of antennas and number of subcarriers. Especially for symbol detection process, LS-MIMO SC-FDMA system with linear detector requires to perform a large matrix inverse computation. Even though linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance, they still introduce high computational complexity and obliviously involve in the computation of matrix inversion. Therefore, a design of complexity reduction algorithm based near-optimal detector for LS-MIMO SC-FDMA system attains research interest. The improved Newton algorithm is employed to obtain linear detection solution which iteratively performs matrix free-inversion operation. The new algorithm performs matrix–matrix multiplication into matrix–vector multiplication, which substantially reduces receiver detection complexity. The efficacy of the proposed method is investigated at 16-QAM. Both ZF and MMSE criteria are proposed and compared through simulations. Simulation results illustrate that the proposed scheme outperforms the conventional detection schemes and exhibits near-optimal performance with a small number of iterations. Further, bit-error-rate performance is closer to classical linear detector with affordable computational complexity. [ABSTRACT FROM AUTHOR]

Introduction: There is an increased prevalence of cognitive impairment (CI) in patients with type 2 diabetes; thus, there is a need for a sensitive and a convenient screening tool for its detection. While Mini-Mental State Examination (MMSE) is a widely used tool for the assessment of cognition, it is not available for free for research. Hindi version of Addenbrooke's Cognitive Examination III is a freely available cognition assessment tool that has been widely used on diverse group of diseases including diabetes. Aims and objectives: To assess cognitive function in patients with type 2 diabetes using Hindi version of ACE-III and compare its performance with MMSE. Materials and methods: Cognition was assessed using validated Hindi version of ACE-III and MMSE among 54 participants with type 2 diabetes and similar number of age, sex, and educational status matched healthy controls. The cut-off for CI was taken at 82/100 for ACE-III, and for MMSE, education level–based cut-offs were used: 21 for the illiterate group, 22 for the low education group, 23 for the middle education group, and 24 for the high education group. Results: Mean age of the study population was 64.5 ± 5.3 years. The average duration of diabetes was 8.5 ± 6.0 years with mean HbA1C of 8.8 ± 2.5%. Both the tools showed high prevalence of CI among cases, but ACE-III was more sensitive in detection of CI when compared to MMSE (64.8% vs. 55.6%). There was a good agreement between ACE-III and MMSE outcomes (Cohen's kappa = 0.732). Conclusion: Type 2 diabetes mellitus is an independent risk factor for cognitive decline in elderly patients. ACE-III is an easy to use, sensitive, and freely available screening tool for cognition assessment in diabetes population. [ABSTRACT FROM AUTHOR]

The objective of the study was to explore the relationship between the plasma levels of 22 metals and cognition status in older adults aged 60 years and above. A cross-sectional survey was conducted between 2018 and 2019. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect the concentrations of metals, and a mini-mental state examination (MMSE) questionnaire was used to estimate the cognition status of the elderly. Based on the years of education and MMSE scores, the participants were separated into the normal and impaired cognition groups. Lasso regression, logistic regression, and restricted cubic spline models were used to explore the relationship between the metals and cognitive status. A total of 1667 subjects were included in the study, and 333 (19.97%) of the participants had impaired cognition. Then, 12 metals, including Al, Fe, Ni, Cu, As, Se, Rb, Sr, Mo, Cd, Sn, and Sb were selected by lasso regression. Before the multivariate adjustment, Al and Cu were associated with the risk of increasing cognitive impairment (OR = 1.756, 95% CI: 1.166–2.646, P = 0.007; OR = 1.519, 95% CI: 1.050–2.197, P = 0.026, respectively). By contrast, Rb was associated with a decrease in the risk of cognitive impairment (OR = 0.626, 95% CI: 0.427–0.918, P = 0.017), but Cd was significantly associated with an increase in this risk (OR = 1.456, 95% CI: 1.003–2.114, P = 0.048). After multivariate adjustment, only Al (OR = 1.533, 95% CI: 1.000–2.350, P = 0.050) maintained a borderline difference with the risk of cognitive impairment. A significant positive correlation was found between the risk of cognitive impairment and Al, Cu, and Cd, contrary to the negative correlation found with Rb. [ABSTRACT FROM AUTHOR]

Multiple input multiple output (MIMO) dual-hop relay systems in delay spread channels are analyzed in this work. The delay spread channel caused by multipath phenomenon in radio propagation is a major reason of intersymbol interference (ISI), increasing bit error ratio (BER) significantly at large enough delay time. To solve this problem, a joint of linear precoder and decoder is designed using unweighted minimum mean-squared error (MMSE) criterion for the system in two proposed schemes. The relay in Scheme 1 has none of the precoder and the decoder whereas that in Scheme 2 has both of them. System performance is evaluated in both perfect and imperfect channel state information (CSI) conditions. With the perfect CSI, the system is analyzed under effects of parameters such as modulation order, data block size, and delay tap numbers to form the basis for selecting their values. With the imperfect CSI, factors including correlation coefficients and estimation error variances are considered to evaluate their effects on the system performance. Simulation results show that BER of Scheme 1 is better with the perfect CSI but more sensitive to CSI errors than that of Scheme 2 is. Which scheme is suitable for a practical system it depends on a trade-off between complexity of the system and accuracy of the channel estimation. [ABSTRACT FROM AUTHOR]

Aim: Mini-Mental State Examination (MMSE) is one of the most used tests for the screening of global cognition in patients with neurological and medical disorders. Norms for the Italian version of the test were published in the 90 s; more recent norms were published in 2020 for Southern Italy only. In the present study, we computed novel adjustment coefficients, equivalent scores and cut-off value for Northern Italy (Lombardia and Veneto) and Italian speaking Switzerland.Methods: We recruited 361 healthy young and old (range: 20-95 years) individuals of both sexes (men: 156, women: 205) and from different educational levels (range: 4-22 years). Neuropsychiatric disorders and severe medical conditions were excluded with a questionnaire and cognitive deficits and were ruled out with standardized neuropsychological tests assessing the main cognitive domains. We used a slightly modified version of MMSE: the word 'fiore' was replaced with 'pane' in verbal recalls to reduce the common interference error 'casa, cane, gatto'. The effect of socio-demographic features on performance at MMSE was assessed via multiple linear regression, with test raw score as dependent variable and sex, logarithm of 101-age and square root of schooling as predictors.Results: Mean raw MMSE score was 28.8 ± 1.7 (range: 23-30). Multiple linear regression showed a significant effect of all socio-demographic variables and reported a value of R2 = 0.26. The new cut off was ≥ 26 /30.Conclusion: We provide here updated norms for a putatively more accurate version of Italian MMSE, produced in a Northern population but potentially valid all over Italy. [ABSTRACT FROM AUTHOR]

Background: Cognitive screening is important for the oldest-old (age 90 +). This age group is the fastest growing and has the highest risk of dementia. However, norms and score equivalence for screening tests are lacking for this group. Aims: To provide norms and score equivalence for commonly used cognitive screening tests for the oldest-old. Methods: Data on 157 participants of the Center for Healthy Aging Longevity Study aged 90 + were analyzed. First, we derived norms for (1) subtests and cognitive domains of the in-person Montreal Cognitive Assessment having a maximum score of 30 (MoCA-30) and (2) the total MoCA-22 score, obtained from the in-person MoCA-30 by summing the subtests that do not require visual input to a maximum score of 22. These norms were derived from 124 participants with a Mini-Mental State Examination (MMSE) ≥ 27. Second, we derived score equivalences for MMSE to MoCA-30 and MoCA-22, and MoCA-30 to MoCA-22 using equipercentile equating method with log-linear smoothing, based on all 157 participants. Results: MoCA-22 total score norms are: mean = 18.3(standard deviation = 2.2). An MMSE score of 27 is equivalent to a MoCA-30 score of 22 and a MoCA-22 score of 16. Discussion and conclusions: Subtest, domain and MoCA-22 norms will aid in evaluation of the oldest-old who cannot complete the MoCA-30 or are tested over the phone. The equivalences of the three cognitive tests (MMSE, MoCA-30, MoCA-22) in the oldest-old will facilitate continuity of cognitive tracking of individuals tested with different tests over time and comparison of the studies that use different cognitive tests. [ABSTRACT FROM AUTHOR]

To examine the association between psychological resilience and cognitive function and investigate the role of acute inflammation as an effect modifier. Total 7535 people from the Cardiovascular and Metabolic Disease Etiology Research Center (CMERC), aged ≥ 50 years and residing in areas near Seoul, South Korea, were included in this cross-sectional analysis. Stressful life events in the past 6 months were gauged by the Life Experience Survey, and current depression symptoms were analyzed with the Beck Depression Inventory-II. Participants were categorized into the following four groups according to their past experience and depression status: reference, resilient, reactive depression, and vulnerable depression. Cognitive function was evaluated using the mini-mental state examination (MMSE). The level of high-sensitivity C-reactive protein (hsCRP) was measured from blood samples. A generalized linear model was used. Upon adjusting for socio-demographic factors, comorbidity, and lifestyle factors, the final model was stratified with the highest quartile of the hsCRP level by sex. Compared to the reference group, the resilient group showed higher MMSE, which was also significant in women (adj-β = 0.280, p-value < 0.001). Vulnerable depression group showed a significantly lower MMSE (adj-β = − -0.997, p-value 0.002), especially in men. This pattern seemed to be limited to the low hsCRP subgroup. We provided evidence from the largest Korean population used to evaluate the association between psychological resilience and cognition, which was more prominent in low inflammatory status. Psychological resilience was associated with a lower likelihood of cognitive deficit in women. This pattern was modulated by inflammatory status. [ABSTRACT FROM AUTHOR]

In this study, we present the results of theoretical analysis and computer simulation of the application of non-Gaussian approximation in demodulation problems in MIMO systems. The results of this study can also be applied in multiuser demodulation problems in systems with non-orthogonal signals. As a result of theoretical analysis, an approximation of a priori distribution of the estimated parameters, as well as a theoretical justification for the application of methods for solving systems of linear and nonlinear equations in solving the problem of demodulation in multi-user systems, were proposed. The results of modeling by the Monte Carlo method, as well as comparison with known demodulation algorithms for MIMO systems, which confirm the effectiveness of the proposed theoretical approach of non-Gaussian approximation for additional consideration of a priori information during demodulation, are presented. [ABSTRACT FROM AUTHOR]

By comparing 51 autistic adults and 49 age-matched controls (aged 30–73 years) we tested if (1) the Montreal Cognitive Assessment (MoCA) is more sensitive in measuring cognitive impairments than the Mini Mental State Examination (MMSE) and (2) if we can replicate the MoCA-findings of Powell et al. (2017) with the Dutch MoCA(-NL). Results showed that: (1) The MoCA-NL is more sensitive, and (2) like Powell, no group differences were observed on the MoCA-NL. However, in contrast to Powell, we did not observe that older autistic adults show more impairment than controls on the MoCA-NL. Nonetheless, as the MoCA-NL is more sensitive to cognitive impairment, it is the recommended screener for older autistic adults. [ABSTRACT FROM AUTHOR]

One of the motivations towards the 5G cellular networks and beyond is the large increase in the supported data rate in both uplink and downlink. Deployment of small size cells, massive MIMO, wherein a large number of antennas are deployed at transmitters and receiver, are effective tools in these networks. Users under cell massive MIMO networks "mMIMO" can provide high data rate. However, the cell-edge user and users, under shadowing, can have a poor performance. Cell-Free networks can provide a satisfied data rate even for shadowed and cell-edge users. In this paper, cell-Free mMIMO networks are mathematically analyzed applying different cooperation mechanisms among the access points APs. Moreover, a new mathematical formula for the throughput, based on BER, is derived. Furthermore, closed formulas for the throughput-BER as well as energy efficiency (EE) performance are derived. Finally, the cognitive radio concept is proposed in order to limit the interference among users accessing the same resources in uplink. Simulation results show that the fully centralized cell-Free mMIMO network has a higher spectral efficiency and energy efficiency than the cellular mMIMO networks at different BER values especially when MMSE is applied. Furthermore, the cognitive radio theory can increase the SE and EE performance of the cell-Free networks when all cooperation mechanisms are applied. [ABSTRACT FROM AUTHOR]

The essential criteria of wireless communication system are accurate signal identification. The channel assessment and adjustment are the two most critical mechanisms for signal identification. Since orthogonal frequency division multiplexing (OFDM) used to decrease bit error rates in current wireless communication systems and to optimize spectral efficiency and compared to a single carrier network, secure channel compensation method. Nevertheless, the system's quality output is degraded depending on the channel state due to the incorrect channel approximation and noise amplification through the channel compensation procedure. In this paper, a 1D Convolutional Neural Network (CNN) deep learning model to estimate the channel provided and the equalized data also recovered. The Deep Neural Network (DNN) can not only use the channel variation features from previous estimates with correctly chosen outputs, but it can also derive additional features from the pilots and receiving signals. To prove the ability of this model the Bit Error Rate (BER) for the recovered data is compared with the conventional models like Minimum Mean Square Error (MMSE) and Least Square (LS) and compared with the Feed Forward Neural Network (FFNN) model in different digital modulation techniques. [ABSTRACT FROM AUTHOR]

Introduction: To assess the value of the Mini-Mental State Examination (MMSE)-score at baseline in predicting survival in adult primary central nervous system lymphoma (PCNSL) patients. Methods: In the HOVON 105/ ALLG NHL 24 phase III study patients with newly-diagnosed PCNSL were randomized between high-dose methotrexate-based chemotherapy with or without rituximab. Data on potential (MMSE-score), and known baseline prognostic factors (age, performance status, serum LDH, cerebrospinal fluid total protein, involvement of deep brain structures, multiple cerebral lesions, and the IELSG-score) were collected prospectively. Multivariable stepwise Cox regression analyses were used to assess the prognostic value of all factors on progression-free survival (PFS) and overall survival (OS) among patients with available MMSE score at baseline. Age was analyzed as continuous variable, the MMSE-score both as a continuous and as a categorical variable. Results: In univariable analysis, age, MMSE-score and whether the patient received rituximab were statistically significantly prognostic factors for PFS. Age and MMSE-score were statistically significantly associated with OS. In a multivariable analysis of the univariately significant factors only MMSE-score was independently associated with the survival endpoints, as a continuous variable (HR for PFS 1.04, 95% CI 1.01–1.08; OS 1.06 (95% CI 1.02–1.10) and as categorical variable HR (< 27 versus ≥ 27 for PFS 1.55 (1.02–2.35); OS 1.68 (1.05–2.70). In our population, performance status, serum LDH, and CSF protein level were not of prognostic value. Conclusion: Neurocognitive disturbances, measured with the MMSE at baseline, are an unfavorable prognostic factor for both PFS and OS in adult PCNSL patients up to 70 years-old. [ABSTRACT FROM AUTHOR]

In this paper, linear precoding (LP) algorithm has been studied to improve the bit error rate (BER) performance of large scale multiple input multiple output (MIMO) system. A new technique of enhancing the BER performance of linear precoding algorithm without complexity in hardware and mathematical calculations is proposed and it is used on a traditional LP algorithms like a Maximum Ratio Transmission (MRT), a Zero-Forcing (ZF), a Regularized Zero-Forcing (RZF), and a Minimum Mean Square Error (MMSE) for Millimeter wave MU-MIMO communication system. A new technique is also used to mitigate the BER gab problem between (ZF, MMSE) and (RZF, MMSE) linear precoding algorithms. The result shows that with using the new technique, the BER performance of modified LP significantly outperform their traditional LP by 10−5 when using 4 element of BS antenna, 10−6 when using 16 element of BS antenna and mitigate the BER gab problem at different signal to noise ratio (SNR). [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved quantum algorithm for the minimum mean square error-based massive multiple-input multiple-output (MIMO) uplink. The new algorithm can reduce the dependency on the assumptions on the input vector, the channel matrix entries and the low rank of the channel matrix, which are indispensable in our previous results. Our improved quantum algorithm applies the quantum block-encoding technology, which depends on the quantum-accessible data structure. Moreover, we design an efficient algorithm for outputting classical data, which makes sure that output data can be utilized in classical devices. Both theoretically mathematical analyses and simulation realizations in massive MIMO systems confirm the applicability of the improved quantum algorithm. With desired precision, and theoretical and numerical analysis, our improved quantum algorithm can achieve a quadratic or even an exponential speedup over classical counterparts. [ABSTRACT FROM AUTHOR]

Large-scale multi-user multiple input multiple output (LS-MU-MIMO) is a promising technology to meet the demands for higher spectral and energy efficiency in the next-generation communication system. LS-MU-MIMO technology can provide 50 times or more network throughput by enhancing the spectral efficiency without cell densification and extra bandwidth requirements. This paper focuses on improvement in the spectral and energy efficiency for a single-cell LS-MU-MIMO system by considering multiple antennas at the user end with the linear precoding schemes. Spectral efficiency of zero-forcing (ZF), minimum mean square error (MMSE) and maximum ratio transmission (MRT) precoding schemes are derived, analyzed and compared for single- and double-antenna users with perfect and imperfect channel state information (CSI). MMSE precoding scheme shows the optimum spectral efficiency as compared to MRT and ZF precoding schemes for a LS-MU-MIMO system. We consider downlink and uplink transmission with various processing techniques at the base station and designed a system model for power consumption to evaluate the energy efficiency of the large MU-MIMO system. We derived energy efficiency expressions for maximal ratio, zero-forcing (ZF) and MMSE processing schemes. Numerical results reveal that the energy efficiency with double-antenna user equipments (UEs) has been enhanced as compared to the UEs having single antenna. Simulation results show that the proposal of adding multiple antennas at the user end can provide a substantial improvement in spectral and energy efficiency as compared to single-antenna users for perfect and imperfect CSI with various precoding schemes. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment in older adults causes a high economic and societal burden. This study assesses the cost-effectiveness of the multicomponent, non-pharmacological MAKS treatment vs. "care as usual" in German day care centers (DCCs) for community-dwelling people with mild cognitive impairment (MCI) or mild to moderate dementia over 6 months. Methods: The analysis was conducted from the societal perspective alongside the cluster-randomized controlled, multicenter, prospective DeTaMAKS-trial with waitlist group design. Outcomes were Mini-Mental Status Examination (MMSE) and Erlangen Test of Activities of Daily Living in Persons with Mild Dementia or Mild Cognitive Impairment (ETAM) of 433 individuals in 32 DCCs. Incremental differences in MMSE and ETAM were calculated via a Gaussian-distributed and incremental cost difference via a Gamma-distributed Generalized Linear Model. Cost-effectiveness was assessed via cost-effectiveness planes and cost-effectiveness acceptability curves (CEAC). Results: At 6 months, MMSE (adjusted mean difference = 0.92; 95% confidence interval (CI): 0.17 to 1.67; p = 0.02) and ETAM (adjusted mean difference = 1.00; CI: 0.14 to 1.85; p = 0.02) were significantly better in the intervention group. The adjusted cost difference was − €938.50 (CI: − 2733.65 to 763.13; p = 0.31). Given the CEAC, MAKS was cost-effective for 78.0% of MMSE and 77.4% for ETAM without a need for additional costs to payers. Conclusions: MAKS is a cost-effective treatment to stabilize the ability to perform activities of daily living and cognitive abilities of people with MCI or mild to moderate dementia in German DCCs. Thus, MAKS should be implemented in DCCs. [ABSTRACT FROM AUTHOR]

Background: Post-stroke depression (PSD) has worse functional outcomes and quality of life. Despite the extensive literature on this topic, there is no agreement on the frequency or risk factors for post-stroke depression. Objectives: To establish the frequency and risk factors of post-stroke depression and its impact on quality of life. Patients and methods: One hundred three stroke patients were recruited from the out-patient clinic of Qena University Hospital who satisfied the WHO definition for stroke, together with a control group of 50 age- and sex-matched healthy volunteers. A complete history, neurological examination, and CT brain were obtained for each patient. DSM-IV TR criteria were used for diagnosis of depressive disorders which was scored with the Hamilton depression rating scale (HAM-D); Barthel Index (BI), and quality of life were also measured. Results: Thirty-eight (36.9%) stroke patients had PSD which was significantly higher than in the normal population (control group 12%). Statistically significant risk factors for PSD included low educational level, low socioeconomic status, smoking, and post-stroke functional impairment. Post-stroke depression has an impact on quality of life. Conclusion: Post-stroke depression is a relatively common complication of stroke and can affect the quality of life. Low educational level and socioeconomic status, as well as smoking and functional impairments, were considered as risk factors for the occurrence of post-stroke depression. Early detection of predictors of post-stroke depression may improve the outcome of stroke and prevent the psychiatric consequences. [ABSTRACT FROM AUTHOR]

In this article, the computational complexity reduction of zero forcing (ZF) and minimum mean square error (MMSE) detection is presented for the uplink multiple input multiple output (MIMO) single carrier frequency division multiple access (SC-FDMA) systems. MIMO SC-FDMA structure with detection of user's data by the well-known multiuser linear detection approaches such as MMSE and ZF often use single input single output detection systems due to its simple detection and significant performance. Although, these approaches involve inversion of a matrix computation whose matrix dimension depend on number of subcarriers used in the system, mainly, it can be few thousands. In practical detection, the computational complexity of matrix inversion becomes very high. Also, the complexity of the receiver is raised because of the superposition of all the transmitted signals at each antenna received in the systems. The proposed conjugate gradient approach reduces the higher computational overhead of linear detectors, which updates iteratively the ZF and MMSE solution and avoids the direct computation of matrix inverse operation. The analysis of the proposed algorithm reveals the superior performance and the low complexity detection in spatial multiplexing SC-FDMA system. Simulations have investigated that the computational complexity of the proposed method has been greatly reduced and bit-error-rate performance is closer to matched filter bound. [ABSTRACT FROM AUTHOR]

Objective: The aims of the present study were: (a) to obtain new normative data of the Italian version of the Mini-Mental Examination State (MMSE) (Measso et al. in Dev Neuropsychol 9:77–85, 1993) by administering the tool to a sample of normal Italian individuals more representative of the current Italian population; (b) to compare the sensitivity of this tool in detecting patients suffering from Alzheimer's disease (AD according to NIA-AA), as compared to the those reported in previous normative Italian studies. Methods: MMSE was administered to 314 normal subjects recruited among individuals (and/or their relatives) attending the Offices of General Practitioners (GP) or Memory Clinics in Campania (Italy) by convenience sampling. A group of 47 patients with AD were included into the study. The effect of demographic variables on the raw MMSE scores of normal subjects was checked by multiple linear regression assuming MMSE scores as dependent variable and age, gender and education as the independent one(s). Therefore, a simultaneous regression model was constructed to correct the raw scores according the sensitive variables. Correction grid and equivalent scores were devised to classify subject's performance. Results: The mean raw MMSE score was 27.78 (SD = 1.80) (range 22–30/30). There was no significant difference between scores achieved by men or women (p = 0.688). Multiple linear regression analysis showed a significant effect of age and years of school attendance on the MMSE raw score, whereas gender did not show any significant effect. The cutoff score, distinguishing between pathological and normal performances, was fixed at the fifth centile corresponding to 24.9/30, higher than the current score of 23.8/30. The new cutoff value was able to identify 44/47 patients with AD, in contrast to 38/47 subjects detected by currently used norms. Conclusions: (1) A more updated and representative population sample; (2) a new cutoff threshold able to distinguish between normal and pathological performances; (3) a correction grid that reduces the risk of false-positive and false-negative values due to the influence of the main demographic factors; (4) greater sensitivity, compared to previous Italian normative studies in identifying people with dementia. [ABSTRACT FROM AUTHOR]

Regarding the massive multiple-input multiple-output (MIMO) uplink, the quantum version detection based on the minimum mean square error criterion is developed for complexity consideration. Specifically, a comprehensive quantum algorithm for encoding the estimated signal into a quantum state is presented, synthesizing the subroutines of state preparation, Hamiltonian simulation, phase estimation and so on. Indispensable assumptions along with the quantum algorithm are summarized and assumption testing in a MIMO scenario is conducted. Both theoretical analyses from the mathematical point of view and simulated realizations in massive MIMO systems basically confirm the applicability of the quantum algorithm. With desired precision, the quantum algorithm achieves almost quadratic speedup over all classical counterparts. Whereas, with weaker precision accompanied by undetermined performance loss, the quantum algorithm eventually breaks through polynomial-time detection complexity and achieves exponential speedup. Future work will be directed toward the error dependence improvement, classical form outputs and experimental applications in quantum communication systems. [ABSTRACT FROM AUTHOR]

Background and aims: Among older adults, olfactory dysfunction is associated with cognitive impairment, lower quality of life, and increased mortality. While age is a risk factor for olfactory dysfunction, other risk factors are less well understood, and may vary between ethno-regional groups. This study investigated how associations between odour identification (OI) and various risk factors, as well as cognition and language ability, differed or were similar in two distinct ethno-regional groups of older adults. Methods: This cross-sectional study used data from two cohorts: 470 Indonesians (aged 67.4 ± 7.4 years) and 819 white Australians (aged 78.7 ± 4.8 years). Univariate and multivariate analyses explored whether OI test scores were associated with age, sex, education, cholesterol levels, apolipoprotein E ε4 status, smoking, diabetes, hypertension and depression scale scores, or with Mini-Mental State Examination (MMSE) and language test performance. Results: Univariate analyses identified some factors associated with OI scores in both Indonesians and white Australians, including older age and smoking with lower scores, and MMSE and language test performance with higher scores. Multivariate analyses yielded different and mutually exclusive patterns of associations in the two ethno-regional groups, with language test scores significantly associated with higher OI scores in Indonesians, and age, being male, smoking, having diabetes and higher depression scale scores significantly associated with lower OI scores in white Australians. Conclusion: Ethno-regional differences may need consideration in the attempt to fully understand associations between OI and negative outcomes like dementia and mortality, and interventions for olfactory dysfunction might need to be tailored to specific ethno-regional groups. However, the difference in mean age between cohorts is a limitation of this study, and future studies should aim to compare populations with similar age distributions. [ABSTRACT FROM AUTHOR]

The estimation of the misalignment of an active telescope, from aberrations measured in its focal plane, is a tomographic issue. MMSE-based estimation is successfully used in optical tomography issues, like multiconjugate adaptive optics, where the problem is linear, and the phase perturbation spectrum rapidly decreasing. The implementation of MMSE developed in the case of multiconjugate adaptive optics has been applied to the alignment of active telescopes, although the forward problem is not linear, and the aberration spectrum different. Its performance is characterized by numerical modeling in the case of a TMA type large-field imaging space telescope, and compared to the classical Least-Square approach. The MMSE estimation brings a significant gain in terms of robustness and accuracy. [ABSTRACT FROM AUTHOR]

The major challenge in the current scenario of wireless system is increasing number of users and hence increased co-channel interference within the limited spectrum available for communication. The compromise in terms of user quality and reliability of communication system is evitable from increased number of call drops and busy channels during mobile voice calls. The above mentioned challenge is attributed for increase in the number of users accommodated in the defined spectrum. The paper presents a novel user selection and transmit antenna selection based approach for enhanced reliability in multi-user multiple input multiple output system, which is next generation wireless system. In contrast to existing technique for multiple antennas and which is evolution of regular channel inversion with block diagonalization, the proposed technique considers systematic and optimum deployment of user selection in the system to enhance sum rate or the system capacity. The comparison of algorithms viz. random, norm based and capacity based user selection is presented with its implementation with precoding techniques which is used to minimize co-channel interference. The analysis proposes that, for each selected user if the transmit antennas are chosen with presented algorithm, the sum rate is improved by 17%. Also, the bit error rate performance of linear precoding with user selection is equivalent to non-linear precoding without user selection. [ABSTRACT FROM AUTHOR]

To fulfill the increasing demand for high speed Variable Bit Rate (VBR) broadcast services with reliable Quality of Service, Group Orthogonal-Orthogonal Frequency Division Multiple Access (GO-OFDMA) and Variable Spreading Length Multicarrier Code Division Multiple Access transmission schemes are examined over Land Mobile Satellite (LMS) channel. This paper presents the performance analysis of aforementioned multiple access transmission schemes in terms of computation complexity, Peak-to-Average-Power-Ratio (PAPR) and the average Bit Error Rate (BER) performance over L-band LMS channel. Minimum Mean Square Equalizer (MMSE) equalizer is employed at the receiver. Based on the simulation results, GO-OFDMA stems out to be a better variable rate transmission scheme in respect of both PAPR and BER performance for higher data rate users. Further, GO-OFDMA based VBR transmitter and MMSE equalizer are prototyped on commercially available virtex5 xc5vlx110t–1ff1136 Field Programmable Gate Array (FPGA) device. A block-by-block analysis of hardware resource utilization and power dissipation for the VBR GO-OFDMA transmitter is provided. The achieved baud rate for VBR GO-OFDMA transmission scheme on FPGA is 103.874 Msps. Thus, GO-OFDMA transmission scheme could be appropriate for VBR communication over LMS channel. [ABSTRACT FROM AUTHOR]

In this paper, we propose a minimum mean square error (MMSE) based transceiver design scheme for a downlink multiple-input multiple-output two-tier heterogeneous network with general linear equality per-cell power constraints. Three practical channel models with both perfect and imperfect channel state information are used in simulations. In each channel model, we consider two system configurations, two data transmission schemes and two cellular cooperation scenarios. Our study shows that the proposed MMSE scheme is more flexible than interference alignment (IA) based scheme. For the cases where the IA-type scheme is applicable, the proposed scheme generally outperforms IA-type scheme in terms of average sum rate and bit error rate, but is computationally more complex than the IA-type scheme. [ABSTRACT FROM AUTHOR]

The performance, complexity and effectiveness of various massive MIMO (M-MIMO) detectors are analyzed operating under highly spatial correlated uniform planar arrays (UPA) channels. In such context, M-MIMO systems present a considerable performance degradation and also, in some cases, an increased complexity. Considering this challenging, but realistic practical scenario, various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate (BER) performance trade-off. Specifically, the successive interference cancellation, lattice reduction (LR) and likelihood ascent search (LAS) schemes, as well as a hybrid version combining such structures with conventional linear MIMO detection techniques are comparatively investigated, aiming to improve performance. Hence, to provide a comprehensive analysis, we consider the number of antennas varying in a wide range (from conventional to massive MIMO condition), as well as low and high modulation orders, aiming to verify the potential of each MIMO detection technique according to its performance–complexity trade-off. We have also studied the correlation effect when both transmit and receiver sides are equipped with UPA antenna configurations. The BER performance is verified under different conditions, varying the array configurations, combining the detection techniques, increasing the number of antennas and/or the modulation order, especially aiming a near M-MIMO condition, i.e. up to 64 × 64 and 121 × 121 antennas has been considered. The aggregated LAS technique has demonstrated good performance in scenarios with high number of antennas, while LR and OSIC operates better in high correlated arrangements. [ABSTRACT FROM AUTHOR]

Key Summary Points: Aim: This paper describes and evaluates an m-health application that allows cognitive functions to be tested. Findings: Using speech-processing techniques an Android application of a slightly modified Mini Mental state Examination (MMSE) test was developed. A small-scale pilot study was conducted in a nursing home showing comparable scores between the MMSE app and the (paper) MMSE test. Message: A mobile digital version of the slightly modified MMSE test has the potential to be used as an attractive alternative for the conventional paper version of the test. Purpose: This paper describes an implementation of an Android application that allows cognitive functions to be tested. Methods: The application consists of a slightly modified Mini Mental state Examination (MMSE) test that provides examiner assistance in diagnosing cognitive impairments. The application deploys speech recognition techniques to allow easy and automated scoring of the test. The test results and test-related information are stored in a database providing easy access to data for follow-up and analysis, resulting in overall benefits to the examiner workflow. A small-scale pilot study of 5 months duration was conducted in a nursing home where 15 residents were tested with the MMSE app test and with the (paper) MMSE test with the aim of determining the agreement between the two test methods. Results: The final MMSE test scores, with a maximum score of 30, agree; the differences have a mean of 0.1, a standard deviation of 2.1 and fall in a [− 4, + 4] range as is illustrated in a Bland–Altman analysis. From examiner reflections, the motoric skills of the participant are indicated to contribute strongly to the time benefit of the assessment itself. Conclusions: The findings of this study suggest that the mobile digital version of the slightly modified MMSE test has the potential to be used as an attractive alternative for the conventional paper version of the test. [ABSTRACT FROM AUTHOR]

Background: Since its introduction by Hodkinson in 1972, Abbreviated Mental Test Score (AMTS) and its English and other language versions have been widely used in research and clinical practice alike. However, whether the various versions of AMTS yield equivalent information has never been tested. Methods: We performed cross-sectional assessment of inpatients aged 65+ years with seven AMTS versions and the Mini-Mental State Examination (MMSE) after correction for age and education (MMSEc). We used the MMSEc cut-off score of < 24 as comparator and fitted linear regression models from which we obtained the receiver operating characteristics, and further compared the c-statistics obtained for each version of AMTS. We used Spearman's correlation to check the relation between different AMTS versions. Results: The mean (SD) age of 72 (52.8% women) patients was 76.2 (7.6) years. The average time spent on education was 11.3 (3.5) years. The AMTS score across versions varied between 7.4 (2.0) and 8.2 (1.7). The MMSE averaged 24.1 (4.6) and the MMSEc averaged 25.2 (4.1). We found that the c-statistic across AMTS versions with dichotomised MMSEc as comparator ranged from 0.83 to 0.85 and did not significantly differ from the c-statistic of 0.87 for original AMTS (all p > 0.16). We found AMTS versions to be significantly correlated (all r between 0.83 and 0.99, all p < 0.0001). Conclusions: We found AMTS to be a reliable and useful tool in the screening for possible cognitive impairment. This seems to be true irrespective of whether we use the original test or any of its studied modifications. [ABSTRACT FROM AUTHOR]

Background: Cognitive dysfunction due to delirium or dementia is a common finding in acutely ill geriatric patients, but often remains undetected. A brief and sensitive clinical identification method could prevent errors or complications while evaluating the mental status of elderly patients.Aims: To evaluate the usefulness and clinical implications of the revised simplified short-term memory recall test (STMT-R) in geriatric patients admitted in the emergency department; with age, gender, dementia history, serum albumin, underlying diseases and clinical outcome used as comparative factors.Methods: Mini-mental state examination and STMT-R scores were initially compared and a positive correlation was observed (r = 0.66, p < 0.001). Subsequently, 885 inpatients aged over 50 years underwent STMT-R evaluation between October 2014 and September 2015. We considered as cognitive dysfunction STMT-R scores ≤ 4 of a maximum score of 8.Results: Among enrolled patients, 52.2% were female and the mean age was 78.9 years. There were 159 patients who were unable to complete the test (incomplete testing group). We observed cognitive dysfunction in 460 patients, while 266 did not have cognitive dysfunction. There were significant differences between those with and without cognitive dysfunction in terms of age, dementia history, underlying respiratory diseases, and hospital outcome.Conclusion: Cognitive dysfunction at admission can have a negative effect on the hospital outcomes of elderly patients. Age, a history of dementia and underlying respiratory diseases may also influence cognitive functional decline. [ABSTRACT FROM AUTHOR]

Millimeter wave (mm-wave) communications has been sighted as solution becoming strong technology for the fifth generation networks. That is because, it brings orders of degree higher spectrum than current cellular bands and helps applications that require weak latency. Massive MIMO is used to minimize the severe constraints enjoined by the excessive propagation loss for millimeter wave frequencies. Where, normal implementation of antenna arrays is transmitted to multiple users simultaneously. Due to hardware constraints in mm-wave systems, classical lower frequency precoding techniques are difficult to be applied at mm-wave. A proposed modified hybrid precoding is suggested for downlink massive MIMO mm-wave systems in this paper. The hybrid precoding consists of mixed of analog and digital processing which is based on minimum mean square error (MMSE) algorithm. Where, quantized beamstearing codebooks are used to produce the analog and digital beamforming vectors with small training and feedback overhead. Extensive simulation programs are executed to compare the performance of the proposed precoding scheme with analog beamforming performance at different digital baseband precoders such as zero forcing precoder. Moreover, studies on the impact of modified hybrid beamforming parameters on the system performance are done. These parameters such as; the number of BS antennas, the number of mobile station antennas, number of effective channel quantization bits and number of RF beamforming quantization bits. Besides, the impact of rate threshold on the coverage probability is studied under different values of NBS antennas. It is shown that the performance of the modified hybrid precoding using MMSE is very close to the single user performance with only 0.5 b/s/Hz performance degradation at SNR = − 5 dB. [ABSTRACT FROM AUTHOR]

Multicarrier code division multiple access (MC-CDMA) is a novel wireless communication technology with high spectral efficiency and system performance. However, all multiple access techniques including MC-CDMA were most likely to have multiple access interference (MAI). So, this paper mainly aims at designing a suitable receiver for MC-CDMA system to mitigate such MAI. The classical receivers like maximal-ratio combining and minimum mean square error fail to cancel MAI when the MC-CDMA is subjected to nonlinear distortions, which may occur due to saturated power amplifiers or arbitrary channel conditions. Being highly nonlinear structures, the neural network (NN) receivers such as multilayer perceptron and radial basis function networks could be better alternative for such a case. The possibility NN receiver for a MC-CDMA system under different nonlinear conditions has been studied with respect to both performance and complexity analysis. [ABSTRACT FROM AUTHOR]

In this paper, a group-based fast symbol detection scheme is proposed for massive Group-wise Space-Time Block Code Multiple-Input Multiple-Output systems. The proposed scheme first chooses a number G, (e.g. G=4,6,8) as the size of each group and then conducts the Minimum Mean Square Error (MMSE) detectors to facilitate symbol detection group by group. In particular, instead of directly using huge computational complexity to compute the MMSE detectors, the proposed scheme recursively finds the corresponding assistant matrices to determine the groups and calculate the MMSE detectors. As a result, the transmitted symbols can be recursively estimated group by group. Finally, simulation results and complexity analysis show the proposed scheme performs very close to the existing methods, while using lower computational complexity. [ABSTRACT FROM AUTHOR]

The purposes of this review were to give the optimal cutoffs of the Montreal Cognitive Assessment (MoCA) by comparing sensitivity and specificity under different cutoffs and compare the MoCA with other screening tools in post-stroke cognitive impairment (PSCI) determined by a neuropsychological evaluation. Articles were derived from a systematic search in PubMed, Web of science, Embase, and CINAHL and were assessed for internal validity by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The figure of risk of bias was made by Review Manager 5.3, and data of selected studies were synthesized by MetaDisc 1.4. Twelve diagnostic studies, involving 2130 patients, were included. The area under the curve (AUC) under cutoffs of 20v19, 21v20, and 26v25 are 0.90, 0.90, and 0.95, showing high predictive validity for PSCI screening within 1 month. When the sensitivity and specificity are equal important, the optimal cutoff is 20v19 (Youden Index = 0.58). Compared to the Mini-Mental State Examination (MMSE), the MoCA has higher sensitivity but lower specificity. The optimal cutoff differs in different stages of stroke. Both the MMSE and MoCA are appropriate screening tools for PSCI, and the use of these two tools should be in accordance with the aim of screening. The Addenbrooke's Cognitive Examination-Revised (ACE-R) can act as a supplement for the MoCA. [ABSTRACT FROM AUTHOR]

Various methods are available for channel estimation in the orthogonal frequency division multiplexing and orthogonal frequency and code division multiplexing (OFCDM) based wireless communication schemes. Along with this, the most utilized techniques are namely the minimum mean square error (MMSE) and least square (LS). The process of LS channel estimation method is simple but it occupies a very high mean square error. On the other hand, the performance of MMSE is better than LS in terms of SNR, though it shows high computational complexity. Compared to MMSE and LS based techniques, the combination of MMSE and LS techniques using evolutionary programming reduces the error significantly to receive exact signal. In this study, we propose a hybrid method namely GGWO that includes grey wolf optimization (GWO) and genetic algorithms (GA) for estimate the channel in MIMO-OFCDM schemes. At first, the best channel is estimated using GWO and afterwards, the MMSE and LS are hybridized through GA for calculating the best channel to decrease error. Overall, the GWO and GA contribute in fine tuning the obtained channel scheme so that the channel model is derived further to correlate with the ideal scheme. Our results demonstrate that the proposed scheme is superior to conventional MMSE and LS in terms of BER and SNR. [ABSTRACT FROM AUTHOR]